Papers with Code 아카이브

Fine-Grained Visual Categorization

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크
0
평가표
0
보존된 평가 기록
0

데이터셋

출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.

MTL-AQA

A new multitask action quality assessment (AQA) dataset, the largest to date, comprising of more than 1600 diving samples; contains detailed annotations for fine-grained action recognition, commentary generation, and estimating the AQA score. Videos from multiple angles provided wherever available. Source: What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment

VegFru

VegFru is a domain-specific dataset for fine-grained visual categorization. VegFru categorizes vegetables and fruits according to their eating characteristics, and each image contains at least one edible part of vegetables or fruits with the same cooking usage. Particularly, all the images are labelled hierarchically. The current version covers vegetables and fruits of 25 upper-level categories and 292 subordinate classes. And it contains more than 160,000 images in total and at least 200 images for each subordinate class. Source: https://openaccess.thecvf.com/content_ICCV_2017/papers/Hou_VegFru_A_Domain-Specific_ICCV_2017_paper.pdf Image Source: https://openaccess.thecvf.com/content_ICCV_2017/papers/Hou_VegFru_A_Domain-Specific_ICCV_2017_paper.pdf

FoodX-251

FoodX-251 is a dataset of 251 fine-grained classes with 118k training, 12k validation and 28k test images. Human verified labels are made available for the training and test images. The classes are fine-grained and visually similar, for example, different types of cakes, sandwiches, puddings, soups, and pastas. Source: FoodX-251: A Dataset for Fine-grained Food Classification Image Source: Kaur et al

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.